Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations126806
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.3 MiB
Average record size in memory160.0 B

Variable types

Numeric15
Text1
Categorical2
DateTime1

Alerts

Avg_Air_Temp_(F) is highly overall correlated with Avg_Rel_Hum_(%) and 7 other fieldsHigh correlation
Avg_Rel_Hum_(%) is highly overall correlated with Avg_Air_Temp_(F) and 4 other fieldsHigh correlation
Avg_Soil_Temp_(F) is highly overall correlated with Avg_Air_Temp_(F) and 6 other fieldsHigh correlation
Avg_Vap_Pres_(mBars) is highly overall correlated with Avg_Air_Temp_(F) and 3 other fieldsHigh correlation
Avg_Wind_Speed_(mph) is highly overall correlated with Wind_Run_(miles)High correlation
Dew_Point_(F) is highly overall correlated with Avg_Air_Temp_(F) and 3 other fieldsHigh correlation
ETo_(in) is highly overall correlated with Avg_Air_Temp_(F) and 6 other fieldsHigh correlation
Max_Air_Temp_(F) is highly overall correlated with Avg_Air_Temp_(F) and 6 other fieldsHigh correlation
Max_Rel_Hum_(%) is highly overall correlated with Avg_Rel_Hum_(%) and 1 other fieldsHigh correlation
Min_Air_Temp_(F) is highly overall correlated with Avg_Air_Temp_(F) and 6 other fieldsHigh correlation
Min_Rel_Hum_(%) is highly overall correlated with Avg_Rel_Hum_(%) and 3 other fieldsHigh correlation
Sol_Rad_(Ly/day) is highly overall correlated with Avg_Air_Temp_(F) and 4 other fieldsHigh correlation
Wind_Run_(miles) is highly overall correlated with Avg_Wind_Speed_(mph)High correlation
Target is highly imbalanced (79.1%) Imbalance
Precip_(in) is highly skewed (γ1 = 20.41722826) Skewed
Precip_(in) has 103732 (81.8%) zeros Zeros

Reproduction

Analysis started2025-03-15 22:51:09.946364
Analysis finished2025-03-15 22:51:58.623991
Duration48.68 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Stn_Id
Real number (ℝ)

Distinct143
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.26245
Minimum2
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:51:58.802060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15
Q199
median171
Q3219
95-th percentile252
Maximum262
Range260
Interquartile range (IQR)120

Descriptive statistics

Standard deviation72.599386
Coefficient of variation (CV)0.46164476
Kurtosis-0.93529961
Mean157.26245
Median Absolute Deviation (MAD)56
Skewness-0.46017357
Sum19941822
Variance5270.6708
MonotonicityNot monotonic
2025-03-15T18:51:59.147456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 992
 
0.8%
91 990
 
0.8%
2 989
 
0.8%
90 989
 
0.8%
204 989
 
0.8%
235 988
 
0.8%
150 986
 
0.8%
195 986
 
0.8%
158 985
 
0.8%
225 984
 
0.8%
Other values (133) 116928
92.2%
ValueCountFrequency (%)
2 989
0.8%
5 980
0.8%
6 949
0.7%
7 963
0.8%
12 981
0.8%
13 920
0.7%
15 973
0.8%
35 964
0.8%
39 984
0.8%
41 867
0.7%
ValueCountFrequency (%)
262 205
 
0.2%
261 368
 
0.3%
260 360
 
0.3%
259 404
0.3%
258 628
0.5%
257 707
0.6%
256 468
0.4%
255 780
0.6%
254 873
0.7%
253 955
0.8%
Distinct143
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:00.128264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length10.189952
Min length4

Characters and Unicode

Total characters1292147
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFivePoints
2nd rowFivePoints
3rd rowFivePoints
4th rowFivePoints
5th rowFivePoints
ValueCountFrequency (%)
ii 7780
 
3.8%
san 6689
 
3.3%
santa 5440
 
2.7%
valley 5385
 
2.6%
south 2902
 
1.4%
east 2846
 
1.4%
west 2836
 
1.4%
lake 2794
 
1.4%
island 2762
 
1.4%
springs 2133
 
1.0%
Other values (174) 162227
79.6%
2025-03-15T18:52:01.259790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 136630
 
10.6%
e 115232
 
8.9%
o 86380
 
6.7%
l 82097
 
6.4%
n 77942
 
6.0%
r 77387
 
6.0%
76988
 
6.0%
i 71036
 
5.5%
t 65416
 
5.1%
s 54074
 
4.2%
Other values (45) 448965
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1292147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 136630
 
10.6%
e 115232
 
8.9%
o 86380
 
6.7%
l 82097
 
6.4%
n 77942
 
6.0%
r 77387
 
6.0%
76988
 
6.0%
i 71036
 
5.5%
t 65416
 
5.1%
s 54074
 
4.2%
Other values (45) 448965
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1292147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 136630
 
10.6%
e 115232
 
8.9%
o 86380
 
6.7%
l 82097
 
6.4%
n 77942
 
6.0%
r 77387
 
6.0%
76988
 
6.0%
i 71036
 
5.5%
t 65416
 
5.1%
s 54074
 
4.2%
Other values (45) 448965
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1292147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 136630
 
10.6%
e 115232
 
8.9%
o 86380
 
6.7%
l 82097
 
6.4%
n 77942
 
6.0%
r 77387
 
6.0%
76988
 
6.0%
i 71036
 
5.5%
t 65416
 
5.1%
s 54074
 
4.2%
Other values (45) 448965
34.7%

CIMIS_Region
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
San Joaquin Valley
23856 
San Francisco Bay
12186 
South Coast Valleys
12025 
Los Angeles Basin
11818 
Monterey Bay
11495 
Other values (9)
55426 

Length

Max length27
Median length25
Mean length17.629182
Min length6

Characters and Unicode

Total characters2235486
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSan Joaquin Valley
2nd rowSan Joaquin Valley
3rd rowSan Joaquin Valley
4th rowSan Joaquin Valley
5th rowSan Joaquin Valley

Common Values

ValueCountFrequency (%)
San Joaquin Valley 23856
18.8%
San Francisco Bay 12186
9.6%
South Coast Valleys 12025
9.5%
Los Angeles Basin 11818
9.3%
Monterey Bay 11495
9.1%
Sacramento Valley 11378
9.0%
Central Coast Valleys 9802
7.7%
Imperial/Coachella Valley 9295
 
7.3%
North Coast Valleys 6712
 
5.3%
Northeast Plateau 6619
 
5.2%
Other values (4) 11620
9.2%

Length

2025-03-15T18:52:01.461843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valley 44529
13.5%
san 40718
12.4%
coast 28539
 
8.7%
valleys 28539
 
8.7%
joaquin 23856
 
7.2%
bay 23681
 
7.2%
basin 12261
 
3.7%
francisco 12186
 
3.7%
south 12025
 
3.7%
los 11818
 
3.6%
Other values (14) 91074
27.7%

Most occurring characters

ValueCountFrequency (%)
a 305444
13.7%
l 211920
 
9.5%
202863
 
9.1%
e 183537
 
8.2%
o 149930
 
6.7%
n 143752
 
6.4%
s 113894
 
5.1%
y 108244
 
4.8%
t 105081
 
4.7%
r 87828
 
3.9%
Other values (24) 622993
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2235486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 305444
13.7%
l 211920
 
9.5%
202863
 
9.1%
e 183537
 
8.2%
o 149930
 
6.7%
n 143752
 
6.4%
s 113894
 
5.1%
y 108244
 
4.8%
t 105081
 
4.7%
r 87828
 
3.9%
Other values (24) 622993
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2235486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 305444
13.7%
l 211920
 
9.5%
202863
 
9.1%
e 183537
 
8.2%
o 149930
 
6.7%
n 143752
 
6.4%
s 113894
 
5.1%
y 108244
 
4.8%
t 105081
 
4.7%
r 87828
 
3.9%
Other values (24) 622993
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2235486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 305444
13.7%
l 211920
 
9.5%
202863
 
9.1%
e 183537
 
8.2%
o 149930
 
6.7%
n 143752
 
6.4%
s 113894
 
5.1%
y 108244
 
4.8%
t 105081
 
4.7%
r 87828
 
3.9%
Other values (24) 622993
27.9%

Date
Date

Distinct991
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2018-01-01 00:00:00
Maximum2020-09-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-15T18:52:01.721507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:52:01.896184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ETo_(in)
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15774837
Minimum0
Maximum0.49
Zeros1225
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:02.110219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.08
median0.15
Q30.23
95-th percentile0.3
Maximum0.49
Range0.49
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.086876584
Coefficient of variation (CV)0.55072888
Kurtosis-0.91772507
Mean0.15774837
Median Absolute Deviation (MAD)0.07
Skewness0.1596635
Sum20003.44
Variance0.0075475408
MonotonicityNot monotonic
2025-03-15T18:52:02.295800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07 5282
 
4.2%
0.06 5221
 
4.1%
0.1 4947
 
3.9%
0.08 4784
 
3.8%
0.21 4652
 
3.7%
0.2 4628
 
3.6%
0.11 4619
 
3.6%
0.09 4600
 
3.6%
0.22 4557
 
3.6%
0.05 4524
 
3.6%
Other values (40) 78992
62.3%
ValueCountFrequency (%)
0 1225
 
1.0%
0.01 2178
1.7%
0.02 2533
2.0%
0.03 3003
2.4%
0.04 3562
2.8%
0.05 4524
3.6%
0.06 5221
4.1%
0.07 5282
4.2%
0.08 4784
3.8%
0.09 4600
3.6%
ValueCountFrequency (%)
0.49 3
 
< 0.1%
0.48 3
 
< 0.1%
0.47 1
 
< 0.1%
0.46 6
 
< 0.1%
0.45 6
 
< 0.1%
0.44 10
 
< 0.1%
0.43 15
 
< 0.1%
0.42 19
 
< 0.1%
0.41 19
 
< 0.1%
0.4 49
< 0.1%

Precip_(in)
Real number (ℝ)

Skewed  Zeros 

Distinct312
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038625933
Minimum0
Maximum13.61
Zeros103732
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:02.482150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum13.61
Range13.61
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20397726
Coefficient of variation (CV)5.2808372
Kurtosis883.67596
Mean0.038625933
Median Absolute Deviation (MAD)0
Skewness20.417228
Sum4898
Variance0.041606724
MonotonicityNot monotonic
2025-03-15T18:52:02.792293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103732
81.8%
0.01 4278
 
3.4%
0.02 3031
 
2.4%
0.04 1480
 
1.2%
0.03 1301
 
1.0%
0.06 920
 
0.7%
0.05 710
 
0.6%
0.07 690
 
0.5%
0.09 603
 
0.5%
0.11 482
 
0.4%
Other values (302) 9579
 
7.6%
ValueCountFrequency (%)
0 103732
81.8%
0.01 4278
 
3.4%
0.02 3031
 
2.4%
0.03 1301
 
1.0%
0.04 1480
 
1.2%
0.05 710
 
0.6%
0.06 920
 
0.7%
0.07 690
 
0.5%
0.08 476
 
0.4%
0.09 603
 
0.5%
ValueCountFrequency (%)
13.61 1
< 0.1%
13.17 1
< 0.1%
12.96 1
< 0.1%
12.4 1
< 0.1%
11.62 1
< 0.1%
9.42 1
< 0.1%
9.2 1
< 0.1%
9.16 1
< 0.1%
8.58 1
< 0.1%
8.1 1
< 0.1%

Sol_Rad_(Ly/day)
Real number (ℝ)

High correlation 

Distinct976
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean458.98889
Minimum0
Maximum6618
Zeros127
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:03.005995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile120
Q1300
median471
Q3634
95-th percentile734
Maximum6618
Range6618
Interquartile range (IQR)334

Descriptive statistics

Standard deviation198.99829
Coefficient of variation (CV)0.43355796
Kurtosis12.74982
Mean458.98889
Median Absolute Deviation (MAD)167
Skewness0.29597772
Sum58202545
Variance39600.318
MonotonicityNot monotonic
2025-03-15T18:52:03.251121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
688 314
 
0.2%
659 305
 
0.2%
684 300
 
0.2%
672 297
 
0.2%
667 297
 
0.2%
707 294
 
0.2%
695 294
 
0.2%
686 294
 
0.2%
690 291
 
0.2%
694 291
 
0.2%
Other values (966) 123829
97.7%
ValueCountFrequency (%)
0 127
0.1%
1 15
 
< 0.1%
2 7
 
< 0.1%
3 10
 
< 0.1%
4 6
 
< 0.1%
5 7
 
< 0.1%
6 7
 
< 0.1%
7 10
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
6618 1
< 0.1%
6081 1
< 0.1%
3731 1
< 0.1%
3197 1
< 0.1%
3095 1
< 0.1%
2901 1
< 0.1%
2767 1
< 0.1%
2571 1
< 0.1%
1908 1
< 0.1%
1582 1
< 0.1%

Avg_Vap_Pres_(mBars)
Real number (ℝ)

High correlation 

Distinct332
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.261447
Minimum0
Maximum37.6
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:03.571319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7
Q18.4
median11.2
Q314
95-th percentile18
Maximum37.6
Range37.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.0898771
Coefficient of variation (CV)0.36317508
Kurtosis0.44852931
Mean11.261447
Median Absolute Deviation (MAD)2.8
Skewness0.33290682
Sum1428019.1
Variance16.727095
MonotonicityNot monotonic
2025-03-15T18:52:03.880002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.2 1304
 
1.0%
12.1 1269
 
1.0%
11 1268
 
1.0%
10.9 1261
 
1.0%
11.6 1247
 
1.0%
12.3 1244
 
1.0%
11.8 1244
 
1.0%
11.7 1244
 
1.0%
11.5 1234
 
1.0%
12.4 1231
 
1.0%
Other values (322) 114260
90.1%
ValueCountFrequency (%)
0 16
< 0.1%
0.1 26
< 0.1%
0.2 9
 
< 0.1%
0.3 6
 
< 0.1%
0.4 2
 
< 0.1%
0.5 5
 
< 0.1%
0.6 2
 
< 0.1%
0.7 6
 
< 0.1%
0.8 6
 
< 0.1%
0.9 6
 
< 0.1%
ValueCountFrequency (%)
37.6 1
< 0.1%
37.4 1
< 0.1%
37.3 1
< 0.1%
37.1 1
< 0.1%
36.7 1
< 0.1%
36.5 2
< 0.1%
36.4 1
< 0.1%
36.3 1
< 0.1%
36.1 1
< 0.1%
35.6 1
< 0.1%

Max_Air_Temp_(F)
Real number (ℝ)

High correlation 

Distinct921
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.169348
Minimum24.9
Maximum123.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:04.054527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum24.9
5-th percentile53.5
Q163.9
median74.2
Q385.9
95-th percentile100
Maximum123.7
Range98.8
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.72334
Coefficient of variation (CV)0.19586893
Kurtosis-0.47349556
Mean75.169348
Median Absolute Deviation (MAD)10.9
Skewness0.16759752
Sum9531924.4
Variance216.77675
MonotonicityNot monotonic
2025-03-15T18:52:04.304434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.1 378
 
0.3%
67.7 372
 
0.3%
67.2 371
 
0.3%
66.1 369
 
0.3%
66.3 365
 
0.3%
74.9 361
 
0.3%
70.1 361
 
0.3%
67.4 359
 
0.3%
67.9 355
 
0.3%
75.1 352
 
0.3%
Other values (911) 123163
97.1%
ValueCountFrequency (%)
24.9 1
< 0.1%
25.5 1
< 0.1%
26.1 1
< 0.1%
26.5 1
< 0.1%
27.1 1
< 0.1%
27.3 2
< 0.1%
27.4 2
< 0.1%
27.6 1
< 0.1%
27.7 2
< 0.1%
27.9 1
< 0.1%
ValueCountFrequency (%)
123.7 1
< 0.1%
123.2 1
< 0.1%
122.7 1
< 0.1%
122.6 2
< 0.1%
122.3 1
< 0.1%
121.2 1
< 0.1%
121 1
< 0.1%
120.8 1
< 0.1%
120.5 1
< 0.1%
119.9 1
< 0.1%

Min_Air_Temp_(F)
Real number (ℝ)

High correlation 

Distinct884
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.42028
Minimum-5
Maximum93.4
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)< 0.1%
Memory size1.9 MiB
2025-03-15T18:52:04.538200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile29.8
Q141
median48.7
Q355.7
95-th percentile66.9
Maximum93.4
Range98.4
Interquartile range (IQR)14.7

Descriptive statistics

Standard deviation11.42892
Coefficient of variation (CV)0.2360358
Kurtosis0.46183219
Mean48.42028
Median Absolute Deviation (MAD)7.3
Skewness-0.02977656
Sum6139982
Variance130.62021
MonotonicityNot monotonic
2025-03-15T18:52:04.757621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.6 541
 
0.4%
49.4 537
 
0.4%
49.9 537
 
0.4%
47.6 535
 
0.4%
50.3 529
 
0.4%
50.8 525
 
0.4%
51.2 525
 
0.4%
48.3 523
 
0.4%
53.7 519
 
0.4%
49.6 518
 
0.4%
Other values (874) 121517
95.8%
ValueCountFrequency (%)
-5 1
< 0.1%
-4.3 2
< 0.1%
-3.6 1
< 0.1%
-2.7 1
< 0.1%
-2.2 2
< 0.1%
-1.8 1
< 0.1%
-1.5 1
< 0.1%
-1 1
< 0.1%
-0.4 1
< 0.1%
0.2 1
< 0.1%
ValueCountFrequency (%)
93.4 2
< 0.1%
92.2 1
 
< 0.1%
92.1 1
 
< 0.1%
92 1
 
< 0.1%
91.8 1
 
< 0.1%
91.6 1
 
< 0.1%
91.4 3
< 0.1%
90.8 1
 
< 0.1%
90.7 1
 
< 0.1%
90.5 2
< 0.1%

Avg_Air_Temp_(F)
Real number (ℝ)

High correlation 

Distinct850
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.098362
Minimum13
Maximum106.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:04.986916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile42.6
Q152.7
median60
Q369
95-th percentile83
Maximum106.5
Range93.5
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation12.380745
Coefficient of variation (CV)0.20263628
Kurtosis0.10156755
Mean61.098362
Median Absolute Deviation (MAD)8.05
Skewness0.27291186
Sum7747638.9
Variance153.28284
MonotonicityNot monotonic
2025-03-15T18:52:05.209537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.8 520
 
0.4%
56 512
 
0.4%
59.5 509
 
0.4%
55.9 509
 
0.4%
55.5 499
 
0.4%
55.1 498
 
0.4%
57.8 495
 
0.4%
57.1 492
 
0.4%
55.7 490
 
0.4%
54.4 489
 
0.4%
Other values (840) 121793
96.0%
ValueCountFrequency (%)
13 1
< 0.1%
14.5 1
< 0.1%
15.2 1
< 0.1%
15.7 1
< 0.1%
15.8 1
< 0.1%
16.1 1
< 0.1%
16.2 1
< 0.1%
16.3 1
< 0.1%
16.5 1
< 0.1%
16.8 2
< 0.1%
ValueCountFrequency (%)
106.5 1
 
< 0.1%
104.8 1
 
< 0.1%
104.2 1
 
< 0.1%
103.8 1
 
< 0.1%
103.4 1
 
< 0.1%
103.3 1
 
< 0.1%
103.1 4
< 0.1%
102.7 1
 
< 0.1%
102.6 1
 
< 0.1%
102.5 3
< 0.1%

Max_Rel_Hum_(%)
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.061519
Minimum0
Maximum100
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:05.458769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q178
median91
Q397
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.36548
Coefficient of variation (CV)0.19239581
Kurtosis1.8344795
Mean85.061519
Median Absolute Deviation (MAD)8
Skewness-1.4838123
Sum10786311
Variance267.82893
MonotonicityNot monotonic
2025-03-15T18:52:05.676731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 14526
 
11.5%
99 8052
 
6.3%
97 6918
 
5.5%
96 6637
 
5.2%
98 6297
 
5.0%
95 6119
 
4.8%
94 5287
 
4.2%
93 4728
 
3.7%
92 4229
 
3.3%
91 3955
 
3.1%
Other values (91) 60058
47.4%
ValueCountFrequency (%)
0 15
< 0.1%
1 16
< 0.1%
2 10
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
100 14526
11.5%
99 8052
6.3%
98 6297
5.0%
97 6918
5.5%
96 6637
5.2%
95 6119
4.8%
94 5287
 
4.2%
93 4728
 
3.7%
92 4229
 
3.3%
91 3955
 
3.1%

Min_Rel_Hum_(%)
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.211504
Minimum0
Maximum100
Zeros354
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:05.859630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q124
median37
Q355
95-th percentile77
Maximum100
Range100
Interquartile range (IQR)31

Descriptive statistics

Standard deviation20.655144
Coefficient of variation (CV)0.51366254
Kurtosis-0.54360436
Mean40.211504
Median Absolute Deviation (MAD)15
Skewness0.45592844
Sum5099060
Variance426.63495
MonotonicityNot monotonic
2025-03-15T18:52:06.437695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 2640
 
2.1%
27 2576
 
2.0%
24 2573
 
2.0%
25 2513
 
2.0%
23 2501
 
2.0%
22 2469
 
1.9%
28 2438
 
1.9%
21 2397
 
1.9%
29 2396
 
1.9%
30 2333
 
1.8%
Other values (91) 101970
80.4%
ValueCountFrequency (%)
0 354
 
0.3%
1 191
 
0.2%
2 57
 
< 0.1%
3 84
 
0.1%
4 135
 
0.1%
5 210
 
0.2%
6 349
 
0.3%
7 505
0.4%
8 677
0.5%
9 902
0.7%
ValueCountFrequency (%)
100 209
0.2%
99 174
0.1%
98 71
 
0.1%
97 93
0.1%
96 96
0.1%
95 112
0.1%
94 96
0.1%
93 125
0.1%
92 164
0.1%
91 169
0.1%

Avg_Rel_Hum_(%)
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.42722
Minimum0
Maximum100
Zeros23
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:06.686049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q146
median63
Q378
95-th percentile91
Maximum100
Range100
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.236392
Coefficient of variation (CV)0.32943689
Kurtosis-0.803198
Mean61.42722
Median Absolute Deviation (MAD)16
Skewness-0.25408745
Sum7789340
Variance409.51156
MonotonicityNot monotonic
2025-03-15T18:52:06.902088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 2441
 
1.9%
81 2386
 
1.9%
77 2361
 
1.9%
78 2354
 
1.9%
76 2351
 
1.9%
80 2338
 
1.8%
74 2274
 
1.8%
82 2253
 
1.8%
75 2251
 
1.8%
73 2251
 
1.8%
Other values (91) 103546
81.7%
ValueCountFrequency (%)
0 23
< 0.1%
1 27
< 0.1%
2 7
 
< 0.1%
3 6
 
< 0.1%
4 9
 
< 0.1%
5 9
 
< 0.1%
6 21
< 0.1%
7 20
< 0.1%
8 22
< 0.1%
9 27
< 0.1%
ValueCountFrequency (%)
100 733
0.6%
99 394
 
0.3%
98 429
 
0.3%
97 510
0.4%
96 594
0.5%
95 696
0.5%
94 790
0.6%
93 900
0.7%
92 1045
0.8%
91 1190
0.9%

Dew_Point_(F)
Real number (ℝ)

High correlation 

Distinct857
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.890738
Minimum-74.3
Maximum82.2
Zeros5
Zeros (%)< 0.1%
Negative174
Negative (%)0.1%
Memory size1.9 MiB
2025-03-15T18:52:07.081479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-74.3
5-th percentile25.8
Q140
median47.5
Q353.5
95-th percentile60.5
Maximum82.2
Range156.5
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation10.912
Coefficient of variation (CV)0.23778218
Kurtosis4.2189231
Mean45.890738
Median Absolute Deviation (MAD)6.6
Skewness-1.1013333
Sum5819220.9
Variance119.07174
MonotonicityNot monotonic
2025-03-15T18:52:07.403742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.8 676
 
0.5%
50 653
 
0.5%
46.7 638
 
0.5%
49 629
 
0.5%
48.8 624
 
0.5%
48.5 619
 
0.5%
50.3 616
 
0.5%
50.2 614
 
0.5%
49.6 612
 
0.5%
48.2 607
 
0.5%
Other values (847) 120518
95.0%
ValueCountFrequency (%)
-74.3 6
< 0.1%
-68.5 2
 
< 0.1%
-64.3 8
< 0.1%
-61 5
< 0.1%
-58.2 4
< 0.1%
-55.8 3
 
< 0.1%
-53.7 3
 
< 0.1%
-51.8 4
< 0.1%
-48.6 2
 
< 0.1%
-45.9 2
 
< 0.1%
ValueCountFrequency (%)
82.2 1
< 0.1%
82 1
< 0.1%
81.9 1
< 0.1%
81.8 1
< 0.1%
81.5 1
< 0.1%
81.3 2
< 0.1%
81.2 1
< 0.1%
81.1 1
< 0.1%
81 1
< 0.1%
80.6 1
< 0.1%

Avg_Wind_Speed_(mph)
Real number (ℝ)

High correlation 

Distinct195
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3212175
Minimum0.7
Maximum46.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:07.638462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile2
Q13
median3.9
Q35.1
95-th percentile8.3
Maximum46.9
Range46.2
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation2.0442375
Coefficient of variation (CV)0.4730698
Kurtosis6.6331739
Mean4.3212175
Median Absolute Deviation (MAD)1
Skewness1.8565708
Sum547956.3
Variance4.1789069
MonotonicityNot monotonic
2025-03-15T18:52:07.842582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 3845
 
3.0%
3.2 3776
 
3.0%
3.4 3747
 
3.0%
3.1 3720
 
2.9%
3 3713
 
2.9%
3.6 3684
 
2.9%
3.7 3579
 
2.8%
3.5 3565
 
2.8%
2.9 3458
 
2.7%
2.8 3345
 
2.6%
Other values (185) 90374
71.3%
ValueCountFrequency (%)
0.7 1
 
< 0.1%
0.9 1
 
< 0.1%
1 750
0.6%
1.1 300
 
0.2%
1.2 256
 
0.2%
1.3 244
 
0.2%
1.4 254
 
0.2%
1.5 373
0.3%
1.6 504
0.4%
1.7 680
0.5%
ValueCountFrequency (%)
46.9 1
< 0.1%
25.1 1
< 0.1%
22.7 1
< 0.1%
21.6 1
< 0.1%
21.3 2
< 0.1%
21.2 1
< 0.1%
21 1
< 0.1%
20.9 1
< 0.1%
20.6 2
< 0.1%
20.5 1
< 0.1%

Wind_Run_(miles)
Real number (ℝ)

High correlation 

Distinct3307
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.70506
Minimum16.2
Maximum1125.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:08.060260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum16.2
5-th percentile48.6
Q171.7
median92.5
Q3122.5
95-th percentile199
Maximum1125.3
Range1109.1
Interquartile range (IQR)50.8

Descriptive statistics

Standard deviation49.056154
Coefficient of variation (CV)0.47303531
Kurtosis6.6346159
Mean103.70506
Median Absolute Deviation (MAD)24.1
Skewness1.8571641
Sum13150424
Variance2406.5063
MonotonicityNot monotonic
2025-03-15T18:52:08.253909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 478
 
0.4%
77 207
 
0.2%
78 202
 
0.2%
76.3 196
 
0.2%
80.3 195
 
0.2%
71 193
 
0.2%
77.3 193
 
0.2%
88.2 193
 
0.2%
87 192
 
0.2%
76.7 191
 
0.2%
Other values (3297) 124566
98.2%
ValueCountFrequency (%)
16.2 1
 
< 0.1%
22.1 1
 
< 0.1%
23.4 1
 
< 0.1%
23.6 1
 
< 0.1%
23.7 1
 
< 0.1%
23.9 5
 
< 0.1%
24 478
0.4%
24.1 70
 
0.1%
24.2 31
 
< 0.1%
24.3 21
 
< 0.1%
ValueCountFrequency (%)
1125.3 1
< 0.1%
603.5 1
< 0.1%
543.9 1
< 0.1%
518.6 1
< 0.1%
511.1 2
< 0.1%
509 1
< 0.1%
503.6 1
< 0.1%
500.4 1
< 0.1%
493.4 2
< 0.1%
492.3 1
< 0.1%

Avg_Soil_Temp_(F)
Real number (ℝ)

High correlation 

Distinct648
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.715666
Minimum31.5
Maximum96.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-15T18:52:08.506083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum31.5
5-th percentile46.9
Q154.4
median62.7
Q370.8
95-th percentile79.8
Maximum96.9
Range65.4
Interquartile range (IQR)16.4

Descriptive statistics

Standard deviation10.679009
Coefficient of variation (CV)0.17027657
Kurtosis-0.3725056
Mean62.715666
Median Absolute Deviation (MAD)8.2
Skewness0.032564918
Sum7952722.8
Variance114.04123
MonotonicityNot monotonic
2025-03-15T18:52:08.761916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.7 549
 
0.4%
53.3 545
 
0.4%
53 512
 
0.4%
55.1 510
 
0.4%
53.5 510
 
0.4%
53.9 507
 
0.4%
54.8 505
 
0.4%
55.5 503
 
0.4%
52.8 501
 
0.4%
55.3 494
 
0.4%
Other values (638) 121670
95.9%
ValueCountFrequency (%)
31.5 2
 
< 0.1%
31.7 2
 
< 0.1%
31.8 2
 
< 0.1%
31.9 2
 
< 0.1%
32 2
 
< 0.1%
32.1 13
< 0.1%
32.2 6
< 0.1%
32.3 3
 
< 0.1%
32.4 5
 
< 0.1%
32.5 6
< 0.1%
ValueCountFrequency (%)
96.9 1
 
< 0.1%
96.8 2
< 0.1%
96.6 1
 
< 0.1%
96.5 2
< 0.1%
96.4 1
 
< 0.1%
96 2
< 0.1%
95.9 2
< 0.1%
95.8 3
< 0.1%
95.6 1
 
< 0.1%
95.5 2
< 0.1%

Target
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
122628 
1
 
4178

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters126806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 122628
96.7%
1 4178
 
3.3%

Length

2025-03-15T18:52:08.961252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T18:52:09.149225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 122628
96.7%
1 4178
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 122628
96.7%
1 4178
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 122628
96.7%
1 4178
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 122628
96.7%
1 4178
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 122628
96.7%
1 4178
 
3.3%

Interactions

2025-03-15T18:51:54.614683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:15.482007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:18.685083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:21.631000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:24.607903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:28.265008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:30.961174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:33.301749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:35.568194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:38.162657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:40.820783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:43.622596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:46.505659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:49.421920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:51.687766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:54.833804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:15.629029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:18.880592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:21.869742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:24.821147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-03-15T18:51:35.415681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:37.934716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:40.653648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:43.413795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:46.359746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:49.240558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:51.568216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-15T18:51:54.422138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-15T18:52:09.304717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Avg_Air_Temp_(F)Avg_Rel_Hum_(%)Avg_Soil_Temp_(F)Avg_Vap_Pres_(mBars)Avg_Wind_Speed_(mph)CIMIS_RegionDew_Point_(F)ETo_(in)Max_Air_Temp_(F)Max_Rel_Hum_(%)Min_Air_Temp_(F)Min_Rel_Hum_(%)Precip_(in)Sol_Rad_(Ly/day)Stn_IdTargetWind_Run_(miles)
Avg_Air_Temp_(F)1.000-0.5210.8830.5900.1560.2360.5910.8220.934-0.4460.885-0.447-0.2970.693-0.0190.1570.156
Avg_Rel_Hum_(%)-0.5211.000-0.3330.307-0.1620.1920.306-0.608-0.6350.824-0.2380.9490.379-0.4460.0400.086-0.162
Avg_Soil_Temp_(F)0.883-0.3331.0000.6560.1240.2040.6570.7680.797-0.3080.843-0.263-0.2560.696-0.0360.1580.124
Avg_Vap_Pres_(mBars)0.5900.3070.6561.0000.0170.1711.0000.3540.4380.2480.7320.346-0.0160.3660.0080.1190.017
Avg_Wind_Speed_(mph)0.156-0.1620.1240.0171.0000.0920.0170.3340.064-0.2030.191-0.0780.0260.2520.0420.0251.000
CIMIS_Region0.2360.1920.2040.1710.0921.0000.1580.1200.1980.1860.2260.1720.0220.0420.3340.1100.092
Dew_Point_(F)0.5910.3060.6571.0000.0170.1581.0000.3540.4380.2480.7320.346-0.0160.3660.0080.1030.017
ETo_(in)0.822-0.6080.7680.3540.3340.1200.3541.0000.823-0.4640.636-0.546-0.3810.937-0.0150.0980.335
Max_Air_Temp_(F)0.934-0.6350.7970.4380.0640.1980.4380.8231.000-0.4670.707-0.619-0.3840.691-0.0310.1640.064
Max_Rel_Hum_(%)-0.4460.824-0.3080.248-0.2030.1860.248-0.464-0.4671.000-0.3060.6880.277-0.3100.0330.069-0.203
Min_Air_Temp_(F)0.885-0.2380.8430.7320.1910.2260.7320.6360.707-0.3061.000-0.131-0.1430.5270.0070.1350.191
Min_Rel_Hum_(%)-0.4470.949-0.2630.346-0.0780.1720.346-0.546-0.6190.688-0.1311.0000.373-0.3960.0370.081-0.078
Precip_(in)-0.2970.379-0.256-0.0160.0260.022-0.016-0.381-0.3840.277-0.1430.3731.000-0.3540.0350.0070.026
Sol_Rad_(Ly/day)0.693-0.4460.6960.3660.2520.0420.3660.9370.691-0.3100.527-0.396-0.3541.000-0.0070.0220.252
Stn_Id-0.0190.040-0.0360.0080.0420.3340.008-0.015-0.0310.0330.0070.0370.035-0.0071.0000.0550.042
Target0.1570.0860.1580.1190.0250.1100.1030.0980.1640.0690.1350.0810.0070.0220.0551.0000.025
Wind_Run_(miles)0.156-0.1620.1240.0171.0000.0920.0170.3350.064-0.2030.191-0.0780.0260.2520.0420.0251.000

Missing values

2025-03-15T18:51:57.440950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-15T18:51:57.985513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Stn_IdStn_NameCIMIS_RegionDateETo_(in)Precip_(in)Sol_Rad_(Ly/day)Avg_Vap_Pres_(mBars)Max_Air_Temp_(F)Min_Air_Temp_(F)Avg_Air_Temp_(F)Max_Rel_Hum_(%)Min_Rel_Hum_(%)Avg_Rel_Hum_(%)Dew_Point_(F)Avg_Wind_Speed_(mph)Wind_Run_(miles)Avg_Soil_Temp_(F)Target
02FivePointsSan Joaquin Valley1/1/20180.060.00219.07.363.435.347.882.046.065.036.63.378.351.10
12FivePointsSan Joaquin Valley1/2/20180.040.00127.07.459.837.747.280.052.067.036.73.174.551.30
22FivePointsSan Joaquin Valley1/3/20180.040.00125.08.461.137.349.979.049.068.039.94.5107.551.30
32FivePointsSan Joaquin Valley1/4/20180.070.01219.011.669.248.756.894.052.074.048.55.8140.253.00
42FivePointsSan Joaquin Valley1/5/20180.070.00239.012.773.847.559.894.049.072.050.84.2101.454.40
52FivePointsSan Joaquin Valley1/6/20180.020.04101.013.460.247.655.296.080.090.052.36.7162.055.40
62FivePointsSan Joaquin Valley1/7/20180.060.00202.011.665.750.956.490.058.074.048.43.583.355.50
72FivePointsSan Joaquin Valley1/8/20180.010.3865.013.860.051.755.697.079.092.053.25.1122.055.90
82FivePointsSan Joaquin Valley1/10/20180.050.00259.010.759.346.654.194.060.075.046.35.7137.656.20
92FivePointsSan Joaquin Valley1/11/20180.020.00121.010.961.345.052.996.064.080.046.92.661.856.50
Stn_IdStn_NameCIMIS_RegionDateETo_(in)Precip_(in)Sol_Rad_(Ly/day)Avg_Vap_Pres_(mBars)Max_Air_Temp_(F)Min_Air_Temp_(F)Avg_Air_Temp_(F)Max_Rel_Hum_(%)Min_Rel_Hum_(%)Avg_Rel_Hum_(%)Dew_Point_(F)Avg_Wind_Speed_(mph)Wind_Run_(miles)Avg_Soil_Temp_(F)Target
128114261GazelleNortheast Plateau9/15/20200.210.0435.06.885.839.363.171.014.034.034.55.7138.065.80
128115261GazelleNortheast Plateau9/16/20200.220.0418.07.583.941.063.669.020.037.037.17.4176.965.40
128116261GazelleNortheast Plateau9/17/20200.210.0452.09.381.342.563.377.026.047.042.86.8163.365.40
128117261GazelleNortheast Plateau9/18/20200.100.0277.011.172.048.861.682.043.059.047.34.6111.565.60
128118262LindenSan Joaquin Valley9/12/20200.100.0338.016.783.250.665.097.046.079.058.41.638.869.20
128120262LindenSan Joaquin Valley9/14/20200.140.0420.015.484.353.766.395.039.070.056.23.481.769.41
128121262LindenSan Joaquin Valley9/15/20200.150.0430.015.186.849.666.096.031.069.055.72.764.269.01
128122262LindenSan Joaquin Valley9/16/20200.150.0445.016.289.053.668.097.030.069.057.63.378.269.01
128123262LindenSan Joaquin Valley9/17/20200.160.0447.016.688.153.370.497.035.065.058.33.378.169.51
128124262LindenSan Joaquin Valley9/18/20200.140.0395.016.181.654.768.495.036.068.057.44.096.770.51